Overview

Dataset statistics

Number of variables8
Number of observations2428
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory154.1 KiB
Average record size in memory65.0 B

Variable types

DateTime1
TimeSeries5
Boolean1
Numeric1

Timeseries statistics

Number of series5
Time series length2428
Starting point2010-01-04 00:00:00
Ending point2019-08-29 00:00:00
Period1 day, 10 hours and 50 minutes
2026-02-05T23:22:43.349668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:43.698164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

repaired? has constant value "False"Constant
adj close is highly overall correlated with close and 3 other fieldsHigh correlation
close is highly overall correlated with adj close and 3 other fieldsHigh correlation
high is highly overall correlated with adj close and 3 other fieldsHigh correlation
low is highly overall correlated with adj close and 3 other fieldsHigh correlation
open is highly overall correlated with adj close and 3 other fieldsHigh correlation
adj close is non stationaryNon stationary
close is non stationaryNon stationary
high is non stationaryNon stationary
low is non stationaryNon stationary
open is non stationaryNon stationary
adj close is seasonalSeasonal
close is seasonalSeasonal
high is seasonalSeasonal
low is seasonalSeasonal
open is seasonalSeasonal
Date has unique valuesUnique
volume has 32 (1.3%) zerosZeros

Reproduction

Analysis started2026-02-05 23:22:39.070114
Analysis finished2026-02-05 23:22:43.250774
Duration4.18 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Date
Date

Unique 

Distinct2428
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size37.9 KiB
Minimum2010-01-04 00:00:00
Maximum2019-08-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-05T23:22:43.865747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:43.986927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

adj close
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1868
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1341.9181
Minimum1050.8
Maximum1888.7
Zeros0
Zeros (%)0.0%
Memory size37.9 KiB
2026-02-05T23:22:44.134292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1050.8
5-th percentile1117.505
Q11222.575
median1289.35
Q31405.45
95-th percentile1719.73
Maximum1888.7
Range837.8999
Interquartile range (IQR)182.87503

Descriptive statistics

Standard deviation180.12929
Coefficient of variation (CV)0.1342327
Kurtosis0.051358142
Mean1341.9181
Median Absolute Deviation (MAD)79.450012
Skewness0.9841677
Sum3258177.2
Variance32446.562
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3287729577
2026-02-05T23:22:44.267772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-05T23:22:44.626537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 14 minutes
std8 hours, 21 minutes and 10.8 seconds
2026-02-05T23:22:45.759706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1273.6999516
 
0.2%
1313.6999515
 
0.2%
1253.8000494
 
0.2%
1209.5999764
 
0.2%
1233.5999764
 
0.2%
1294.6999514
 
0.2%
1293.3000494
 
0.2%
1386.8000494
 
0.2%
1273.4000244
 
0.2%
1229.3000494
 
0.2%
Other values (1858)2385
98.2%
ValueCountFrequency (%)
1050.8000491
< 0.1%
1052.1999511
< 0.1%
1054.1999511
< 0.1%
1056.1999511
< 0.1%
1060.0999761
< 0.1%
1060.3000491
< 0.1%
1061.6999511
< 0.1%
1062.4000241
< 0.1%
1062.9000241
< 0.1%
1063.8000491
< 0.1%
ValueCountFrequency (%)
1888.6999511
< 0.1%
1873.6999511
< 0.1%
1869.9000241
< 0.1%
1858.3000491
< 0.1%
1856.4000241
< 0.1%
1854.4000241
< 0.1%
1848.9000241
< 0.1%
1828.51
< 0.1%
1826.8000491
< 0.1%
1826.6999511
< 0.1%
2026-02-05T23:22:44.391702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

close
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1868
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1341.9181
Minimum1050.8
Maximum1888.7
Zeros0
Zeros (%)0.0%
Memory size37.9 KiB
2026-02-05T23:22:46.488326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1050.8
5-th percentile1117.505
Q11222.575
median1289.35
Q31405.45
95-th percentile1719.73
Maximum1888.7
Range837.8999
Interquartile range (IQR)182.87503

Descriptive statistics

Standard deviation180.12929
Coefficient of variation (CV)0.1342327
Kurtosis0.051358142
Mean1341.9181
Median Absolute Deviation (MAD)79.450012
Skewness0.9841677
Sum3258177.2
Variance32446.562
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3287729577
2026-02-05T23:22:46.625241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-05T23:22:46.978323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 14 minutes
std8 hours, 21 minutes and 10.8 seconds
2026-02-05T23:22:47.960081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1273.6999516
 
0.2%
1313.6999515
 
0.2%
1253.8000494
 
0.2%
1209.5999764
 
0.2%
1233.5999764
 
0.2%
1294.6999514
 
0.2%
1293.3000494
 
0.2%
1386.8000494
 
0.2%
1273.4000244
 
0.2%
1229.3000494
 
0.2%
Other values (1858)2385
98.2%
ValueCountFrequency (%)
1050.8000491
< 0.1%
1052.1999511
< 0.1%
1054.1999511
< 0.1%
1056.1999511
< 0.1%
1060.0999761
< 0.1%
1060.3000491
< 0.1%
1061.6999511
< 0.1%
1062.4000241
< 0.1%
1062.9000241
< 0.1%
1063.8000491
< 0.1%
ValueCountFrequency (%)
1888.6999511
< 0.1%
1873.6999511
< 0.1%
1869.9000241
< 0.1%
1858.3000491
< 0.1%
1856.4000241
< 0.1%
1854.4000241
< 0.1%
1848.9000241
< 0.1%
1828.51
< 0.1%
1826.8000491
< 0.1%
1826.6999511
< 0.1%
2026-02-05T23:22:46.744443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

high
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1876
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1348.7202
Minimum1062
Maximum1911.6
Zeros0
Zeros (%)0.0%
Memory size37.9 KiB
2026-02-05T23:22:48.687400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1062
5-th percentile1124.105
Q11226.575
median1293.75
Q31413.825
95-th percentile1728.265
Maximum1911.6
Range849.59998
Interquartile range (IQR)187.25006

Descriptive statistics

Standard deviation181.89478
Coefficient of variation (CV)0.13486472
Kurtosis0.075994959
Mean1348.7202
Median Absolute Deviation (MAD)78.949951
Skewness0.99597546
Sum3274692.7
Variance33085.711
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3404490557
2026-02-05T23:22:48.820260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-05T23:22:49.374777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 14 minutes
std8 hours, 21 minutes and 10.8 seconds
2026-02-05T23:22:50.359695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1320.6999515
 
0.2%
12365
 
0.2%
12924
 
0.2%
12454
 
0.2%
12474
 
0.2%
13124
 
0.2%
1291.5999764
 
0.2%
12344
 
0.2%
1292.6999514
 
0.2%
1273.54
 
0.2%
Other values (1866)2386
98.3%
ValueCountFrequency (%)
10621
< 0.1%
1064.5999761
< 0.1%
1066.1999511
< 0.1%
1068.4000241
< 0.1%
1068.51
< 0.1%
1069.51
< 0.1%
1070.1999511
< 0.1%
1070.3000491
< 0.1%
1071.51
< 0.1%
1071.9000241
< 0.1%
ValueCountFrequency (%)
1911.5999761
< 0.1%
1909.3000491
< 0.1%
18951
< 0.1%
1884.1999511
< 0.1%
1881.3000491
< 0.1%
1874.4000241
< 0.1%
1873.6999511
< 0.1%
18701
< 0.1%
1853.0999761
< 0.1%
1852.4000241
< 0.1%
2026-02-05T23:22:48.938407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

low
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1908
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1334.8227
Minimum1045.2
Maximum1864
Zeros0
Zeros (%)0.0%
Memory size37.9 KiB
2026-02-05T23:22:51.091531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1045.2
5-th percentile1110
Q11216.2
median1284.3
Q31394.5
95-th percentile1708.725
Maximum1864
Range818.80005
Interquartile range (IQR)178.30008

Descriptive statistics

Standard deviation178.30552
Coefficient of variation (CV)0.13357992
Kurtosis0.032317591
Mean1334.8227
Median Absolute Deviation (MAD)78.900024
Skewness0.97257018
Sum3240949.5
Variance31792.857
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3222464149
2026-02-05T23:22:51.225667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-05T23:22:51.736727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 14 minutes
std8 hours, 21 minutes and 10.8 seconds
2026-02-05T23:22:52.724871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1244.8000495
 
0.2%
1218.5999765
 
0.2%
1222.1999515
 
0.2%
11914
 
0.2%
1279.4000244
 
0.2%
1294.6999514
 
0.2%
12404
 
0.2%
1280.3000494
 
0.2%
1284.3000494
 
0.2%
1281.0999764
 
0.2%
Other values (1898)2385
98.2%
ValueCountFrequency (%)
1045.1999511
< 0.1%
1046.1999511
< 0.1%
1049.5999761
< 0.1%
1050.51
< 0.1%
1051.0999761
< 0.1%
1052.0999761
< 0.1%
1052.6999511
< 0.1%
1058.51
< 0.1%
1058.6999511
< 0.1%
10591
< 0.1%
ValueCountFrequency (%)
18641
< 0.1%
1858.4000241
< 0.1%
18351
< 0.1%
18301
< 0.1%
1828.5999761
< 0.1%
1824.5999761
< 0.1%
1823.6999511
< 0.1%
1814.4000241
< 0.1%
1811.4000241
< 0.1%
18091
< 0.1%
2026-02-05T23:22:51.344966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

open
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1883
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1342.1278
Minimum1052.2
Maximum1909
Zeros0
Zeros (%)0.0%
Memory size37.9 KiB
2026-02-05T23:22:53.449020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1052.2
5-th percentile1118.035
Q11222.825
median1289.35
Q31406.1
95-th percentile1721.04
Maximum1909
Range856.80005
Interquartile range (IQR)183.27499

Descriptive statistics

Standard deviation180.37445
Coefficient of variation (CV)0.13439439
Kurtosis0.058127973
Mean1342.1278
Median Absolute Deviation (MAD)79
Skewness0.9874596
Sum3258686.2
Variance32534.941
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3413155088
2026-02-05T23:22:53.582795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-05T23:22:53.937021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 14 minutes
std8 hours, 21 minutes and 10.8 seconds
2026-02-05T23:22:55.082561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1273.55
 
0.2%
1252.6999515
 
0.2%
1292.0999765
 
0.2%
1279.4000245
 
0.2%
1239.3000494
 
0.2%
1224.8000494
 
0.2%
1256.1999514
 
0.2%
1199.6999514
 
0.2%
1612.8000494
 
0.2%
1285.1999514
 
0.2%
Other values (1873)2384
98.2%
ValueCountFrequency (%)
1052.1999511
< 0.1%
1053.6999511
< 0.1%
1054.4000241
< 0.1%
1056.51
< 0.1%
1061.9000241
< 0.1%
10631
< 0.1%
1063.4000241
< 0.1%
10641
< 0.1%
1064.5999762
0.1%
1064.8000491
< 0.1%
ValueCountFrequency (%)
19091
< 0.1%
1886.3000491
< 0.1%
1868.9000241
< 0.1%
1868.5999761
< 0.1%
1858.0999761
< 0.1%
1852.4000241
< 0.1%
18431
< 0.1%
1839.3000491
< 0.1%
18331
< 0.1%
1830.5999761
< 0.1%
2026-02-05T23:22:53.701785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

repaired?
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.3 KiB
False
2428 
ValueCountFrequency (%)
False2428
100.0%
2026-02-05T23:22:55.738503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

volume
Real number (ℝ)

Zeros 

Distinct905
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5818.563
Minimum0
Maximum386334
Zeros32
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size37.9 KiB
2026-02-05T23:22:55.817031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q135
median125.5
Q3426.25
95-th percentile6977.1
Maximum386334
Range386334
Interquartile range (IQR)391.25

Descriptive statistics

Standard deviation30569.691
Coefficient of variation (CV)5.2538214
Kurtosis48.599208
Mean5818.563
Median Absolute Deviation (MAD)110.5
Skewness6.6976471
Sum14127471
Variance9.3450599 × 108
MonotonicityNot monotonic
2026-02-05T23:22:55.938370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032
 
1.3%
2025
 
1.0%
1424
 
1.0%
823
 
0.9%
423
 
0.9%
723
 
0.9%
523
 
0.9%
2722
 
0.9%
222
 
0.9%
121
 
0.9%
Other values (895)2190
90.2%
ValueCountFrequency (%)
032
1.3%
121
0.9%
222
0.9%
318
0.7%
423
0.9%
523
0.9%
617
0.7%
723
0.9%
823
0.9%
917
0.7%
ValueCountFrequency (%)
3863341
< 0.1%
2908891
< 0.1%
2805461
< 0.1%
2761361
< 0.1%
2754421
< 0.1%
2714571
< 0.1%
2590501
< 0.1%
2544281
< 0.1%
2471681
< 0.1%
2374971
< 0.1%

Interactions

2026-02-05T23:22:42.572796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:40.372087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:40.817403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.251674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.690711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:42.139671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:42.659128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:40.444990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:40.887076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.323964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.763255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:42.209519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:42.743345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:40.517545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:40.957766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.393527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.840887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:42.280153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:42.826972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:40.590339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.026405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.463042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.911143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:42.348842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:42.912642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:40.660644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.095534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.536591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.979562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:42.418075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:42.996554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:40.730244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.165935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:41.605715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:42.048542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T23:22:42.487917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-05T23:22:56.021878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
adj closeclosehighlowopenvolume
adj close1.0001.0000.9980.9980.9960.016
close1.0001.0000.9980.9980.9960.016
high0.9980.9981.0000.9960.9980.028
low0.9980.9980.9961.0000.9980.002
open0.9960.9960.9980.9981.0000.016
volume0.0160.0160.0280.0020.0161.000

Missing values

2026-02-05T23:22:43.127586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-05T23:22:43.206898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Dateadj closeclosehighlowopenrepaired?volume
2010-01-042010-01-041117.6999511117.6999511122.3000491097.0999761117.699951False184
2010-01-052010-01-051118.0999761118.0999761126.5000001115.0000001118.099976False53
2010-01-062010-01-061135.9000241135.9000241139.1999511120.6999511135.900024False363
2010-01-072010-01-071133.0999761133.0999761133.0999761129.1999511133.099976False56
2010-01-082010-01-081138.1999511138.1999511138.1999511122.6999511138.199951False54
2010-01-112010-01-111150.6999511150.6999511161.1999511143.0000001150.699951False177
2010-01-122010-01-121128.9000241128.9000241157.1999511127.1999511128.900024False51
2010-01-132010-01-131136.4000241136.4000241136.4000241121.0000001136.400024False58
2010-01-142010-01-141142.5999761142.5999761145.9000241132.8000491137.000000False81
2010-01-152010-01-151130.0999761130.0999761133.4000241127.1999511132.800049False50
Dateadj closeclosehighlowopenrepaired?volume
2019-08-162019-08-161512.5000001512.5000001524.5999761504.6999511524.599976False1815
2019-08-192019-08-191500.4000241500.4000241507.5999761492.9000241507.199951False205
2019-08-202019-08-201504.5999761504.5999761506.0999761497.5000001497.500000False486
2019-08-212019-08-211504.5999761504.5999761505.0000001498.8000491504.900024False350
2019-08-222019-08-221497.3000491497.3000491497.3000491493.8000491495.099976False686
2019-08-232019-08-231526.5999761526.5999761527.5999761493.5000001493.500000False983
2019-08-262019-08-261526.3000491526.3000491543.3000491524.3000491543.199951False334
2019-08-272019-08-271541.0000001541.0000001542.0999761528.5000001530.500000False166
2019-08-282019-08-281537.8000491537.8000491538.0999761537.8000491538.099976False2756
2019-08-292019-08-291526.5000001526.5000001549.3000491519.5999761537.500000False704